Smart City Gnosys

Smart city article details

Title	Energy-Efficient Federated Learning-Driven Intelligent Traffic Monitoring: Bayesian Prediction And Incentive Mechanism Design
ID_Doc	23481
Authors	Wang Y.; Sui M.; Xia T.; Liu M.; Yang J.; Zhao H.
Year	2025
Published	Electronics (Switzerland), 14, 9
DOI	http://dx.doi.org/10.3390/electronics14091891
Abstract	With the growing integration of the Internet of Things (IoT), low-altitude intelligent networks, and vehicular networks, smart city traffic systems are gradually evolving into an air–ground integrated intelligent monitoring framework. However, traditional centralized model training faces challenges such as high network load due to massive data transmission, energy management difficulties for mobile devices like UAVs, and privacy risks associated with non-anonymized road operation data. Therefore, this paper proposes an air–ground collaborative federated learning framework that integrates Bayesian prediction and an incentive mechanism to achieve privacy protection and communication optimization through localized model training and differentiated incentive strategies. Simulation experiments demonstrate that, compared to the Equal Contribution Algorithm (ECA) and the Importance Contribution Algorithm (ICA), the proposed method improves model convergence speed while reducing incentive costs, providing theoretical support for the reliable operation of large-scale intelligent traffic monitoring systems. © 2025 by the authors.
Author Keywords	Bayesian optimization; energy management; federated learning; incentive mechanism; privacy protection; smart city; traffic flow monitoring

Similar Articles

Id	Similarity	Authors	Title	Published
1664	0.887	Khatua S.; De D.; Maji S.; Maity S.; Nielsen I.E.	A Federated Learning Model For Integrating Sustainable Routing With The Internet Of Vehicular Things Using Genetic Algorithm	Decision Analytics Journal, 11 (2024)
26346	0.875	Nguyen D.C.; Ding M.; Pathirana P.N.; Seneviratne A.; Li J.; Vincent Poor H.	Federated Learning For Internet Of Things: A Comprehensive Survey	IEEE Communications Surveys and Tutorials, 23, 3 (2021)
23970	0.871	Almaazmi K.I.A.; Almheiri S.J.; Khan M.A.; Shah A.A.; Abbas S.; Ahmad M.	Enhancing Smart City Sustainability With Explainable Federated Learning For Vehicular Energy Control	Scientific Reports, 15, 1 (2025)
16402	0.869	Wang D.; Wu M.; Chakraborty C.; Min L.; He Y.; Guduri M.	Covert Communications In Air-Ground Integrated Urban Sensing Networks Enhanced By Federated Learning	IEEE Sensors Journal, 24, 5 (2024)
47130	0.867	Wen T.; Zhang H.; Zhang H.; Wu H.; Wang D.; Liu X.; Zhang W.; Wang Y.; Cao S.	Rtifed: A Reputation Based Triple-Step Incentive Mechanism For Energy-Aware Federated Learning Over Battery-Constricted Devices	Computer Networks, 241 (2024)
7148	0.866	Kulkarni V.; Lakshmi A.S.; Lakshmi C.B.N.; Panneerselvam S.; Kanan M.; Flah A.; Elnaggar M.F.	Air Quality Decentralized Forecasting: Integrating Iot And Federated Learning For Enhanced Urban Environmental Monitoring	Engineering, Technology and Applied Science Research, 14, 4 (2024)
26359	0.865	Gandhi M.; Singh S.K.; Ravikumar R.N.; Vaghela K.	Federated Learning In Secure Smart City Sensing: Challenges And Opportunities	Edge of Intelligence: Exploring the Frontiers of AI at the Edge (2025)
26308	0.863	Sepasgozar S.S.; Pierre S.	Fed-Ntp: A Federated Learning Algorithm For Network Traffic Prediction In Vanet	IEEE Access, 10 (2022)
23480	0.861	Tan C.; Yu P.; Qu Z.; Zhang L.; Li W.; Qiu X.; Guo S.	Energy-Efficient Federated Learning Training Optimization For Digital Twin Driven 6G Air-Ground Integrated Vehicular Networks	IEEE Transactions on Intelligent Transportation Systems (2025)
8048	0.861	Dang T.K.; Tran-Truong P.T.; Trang N.T.H.	An Enhanced Incentive Mechanism For Crowdsourced Federated Learning Based On Contract Theory And Shapley Value	Communications in Computer and Information Science, 1925 CCIS (2023)